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TRCM: a methodology for temporal analysis of evolving concepts in Twitter
The Twitter network has been labelled the most commonly used microblogging application around today. With about 500 million estimated registered users as of June, 2012, Twitter has become a credible medium of sentiment/opinion expression. It is also a notable medium for information dissemination; including breaking news on diverse issues since it was launched in 2007. Many organisations, individuals and even government bodies follow activities on the network in order to obtain knowledge on how their audience reacts to tweets that affect them. We can use postings on Twitter (known as tweets) to analyse patterns associated with events by detecting the dynamics of the tweets. A common way of labelling a tweet is by including a number of hashtags that describe its contents. Association Rule Mining can find the likelihood of co-occurrence of hashtags. In this paper, we propose the use of temporal Association Rule Mining to detect rule dynamics, and consequently dynamics of tweets. We coined our methodology Transaction-based Rule Change Mining (TRCM). A number of patterns are identifiable in these rule dynamics including, new rules, emerging rules, unexpected rules and ?dead' rules. Also the linkage between the different types of rule dynamics is investigated experimentally in this paper
Autonomic Discovery of News Evolvement in Twitter
Recently the continuous increase in data sizes has resulted in many data processing challenges. This increase has compelled data users to find automatic means of looking into databases to bring out vital information. Retrieving information from âBig dataâ, (as it is often referred to) can be likened to finding âa needle in the haystackâ. It is worthy of note that while big data has several computational challenges, it also serves as gateway to technological preparedness in making the world a global village. Social media sites (of which Twitter is one) are known to be big data collectors as well as an open source for information retrieval. Easy access to social media sites and the advancement of technology tools such as the computer and smart devices have made it convenient for different entities to store enormous data in real time. Twitter is known to be the most powerful and most popular microbloging tool in social media. It offers its users the opportunity of posting and receiving instantaneous information from the network. Traditional news media follow the activities on Twitter network in order to retrieve interesting tweets that can be used to enhance their news reports and news updates. Twitter users include hashtags symbols (#) as prefix to keywords used in tweets to describe its content and to enhance the readability of their tweets. This chapter uses the Apriori method for Association Rule Mining (ARM) and a novel methodology termed Rule Type Identification-Mapping (RTI-Mapping) which is inherited from Transaction-based Rule Change Mining TRCM (Adedoyin-Olowe et al., 2013) and Transaction-based Rule Change Mining-Rule Type Identification (TRCM-RTI) (Gomes et al., 2013) to map Association Rules (ARs) detected in tweetsâ hashtags to evolving news reports and news updates of traditional news agents in real life. TRCM uses Association Rule Mining (ARM) to analyse tweets on the same topic over consecutive periods t and tâ+â1 using Rule Matching (RM) to detected changes in ARs such as emerging, unexpected, new and dead rules. This is obtained by setting user-defined Rule Matching Threshold (RMT) to match rules in tweets at time t with those in tweets at tâ+â1 in order to ascertain rules that fall into the different patterns. TRCM-RTI is a methodology built from TRCM, it identifies rule types of evolving ARs present in tweetsâ hashtags at different time periods. This chapter adopts RTI-Mapping from methodologies in (Adedoyin-Olowe et al., 2013) and (Gomes et al., 2013) to map ARs with online evolving news of top traditional news agents in order to detect and track news and news updates of evolving events. This is an initial experiment of ARs mapping to evolving news. The mapping is done manually at this stage and the methodology is validated using four events and news topics as case studies. The experiments show substantial result on the selected news topics